SoroushVahidi/scidocs huggingface dataset:
An Algorithmic Approach to Concept Exploration in a Large Knowledge Network (Automatic Thesaurus Consultation): Symbolic Branch-and-Bound Search vs. Connectionist Hopfield Net Activation
(1995)

This paper presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge-based systems and to alleviate the limitations of the manual browsing approach, we have developed two spreading activation-based algorithms for concept exploration in large, heterogeneous networks of concepts (e.g., multiple thesauri). One algorithm, which is based on the symbolic Al paradigm, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The second algorithm, which is based on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify “convergent” concepts for some initial queries (a parallel, heuristic search process). Both algorithms can be adopted for automatic, multiple-thesauri consultation. We tested these two algorithms on a large text-based knowledge network of about 13,000 nodes (terms) and 80,000 directed links in the area of computing technologies. This knowledge network was created from two external thesauri and one automatically generated thesaurus. We conducted experiments to compare the behaviors and performances of the two algorithms with the hypertext-like browsing process. Our experiment revealed that manual browsing achieved higher-term recall but lower-term precision in comparison to the algorithmic systems. However, it was also a much more laborious and cognitively demanding process. In document retrieval, there were no statistically significant differences in document recall and precision between the algorithms and the manual browsing process. In light of the effort required by the manual browsing process, our proposed algorithmic approach presents a viable option for efficiently traversing largescale, multiple thesauri (knowledge network).
AndroTaint: An efficient android malware detection framework using dynamic taint analysis
(2017)

Android operating system is widespread in today's smart-phone market due to its open source model, its easy functionality and huge number of Apps and App-Stores (the official and others). The coverage of some 90% of the market, it is also the place-holder for user's personal as well as sensitive information (IMEI, IMSI, etc.). There is a tendency of Apps user to trust on Android OS for securing his/her data but it has been proved that Android OS is more vulnerable to exploitation either for fun or for monetary purpose. Malware detection for Android OS has become an upcoming research problem of interest. Dynamic Taint Analysis is an efficient analysis in existing Android malware detection analysis. The aim of this paper is to work towards Dynamic Taint Analysis of Android malware using automatic tagging and with out modification in Android platform. In order to do this, this paper surveys exhaustively the available literature and works related to dynamic taint analysis. This paper proposes some novel ideas to improve the existing solution with more accuracy. There are two phases of our novel algorithm, the first phase is training phase for feature extraction and the second phase is the analysis phase for automatic tagging and tainting. We have developed a framework named AndroTaint, which is working on Dynamic Taint Analysis. AndroTaint follows a novel supervised and unsupervised Anomaly Detection technique with high precision, recall and harmonic mean. Our Dynamic Taint Analysis algorithm categorizes the App is risky, benign, malicious or aggressive as per their feature and behaviour. We have measured AndroTaint effectiveness on the basis of timeline for building dataset and 10-fold cross validation. AndroTaint covers 90% of malware and benign in analysis phase with less false positive and false negative.
Object Detection using Deep Learning
(2019)

Autonomous vehicles, surveillance systems, face detection systems lead to the development of accurate object detection system [1]. These systems recognize, classify and localize every object in an image by drawing bounding boxes around the object [2]. These systems use existing classification models as backbone for Object Detection purpose. Object detection is the process of finding instances of real-world objects such as human faces, animals and vehicles etc., in pictures, images or in videos. An Object detection algorithm uses extracted features and learning techniques to recognize the objects in an image. In this paper, various Object Detection techniques have been studied and some of them are implemented. As a part of this paper, three algorithms for object detection in an image were implemented and their results were compared. The algorithms are “Object Detection using Deep Learning Framework by OpenCV”, “Object Detection using Tensorflow” and “Object Detection using Keras models”.
Comdb2: Bloomberg's Highly Available Relational Database System
(2016)

Comdb2 is a distributed database system designed for geographical replication and high availability. In contrast with the latest trends in this field, Comdb2 o↵ers full transactional support, a standard relational model, and the expressivity of SQL. Moreover, the system allows for rich stored procedures using a dialect of Lua. Comdb2 implements a serializable system in which reads from any node always return current values. Comdb2 provides transparent High Availability through built-in service discovery and sophisticated retry logic embedded in the standard API. In addition to the relational data model, Comdb2 implements queues for publisher-to-subscriber message delivery. Queues can be combined with table triggers for timeconsistent log distribution, providing functionality commonly needed in modern OLTP. In this paper we give an overview of our last twelve years of work. We focus on the design choices that have made Comdb2 the primary database solution within our company, Bloomberg LP (BLP).
DLM: Decentralized Linearized Alternating Direction Method of Multipliers
(2015)

This paper develops the Decentralized Linearized Alternating Direction Method of Multipliers (DLM) that minimizes a sum of local cost functions in a multiagent network. The algorithm mimics operation of the decentralized alternating direction method of multipliers (DADMM) except that it linearizes the optimization objective at each iteration. This results in iterations that, instead of successive minimizations, implement steps whose cost is akin to the much lower cost of the gradient descent step used in the distributed gradient method (DGM). The algorithm is proven to converge to the optimal solution when the local cost functions have Lipschitz continuous gradients. Its rate of convergence is shown to be linear if the local cost functions are further assumed to be strongly convex. Numerical experiments in least squares and logistic regression problems show that the number of iterations to achieve equivalent optimality gaps are similar for DLM and ADMM and both much smaller than those of DGM. In that sense, DLM combines the rapid convergence of ADMM with the low computational burden of DGM.
Table of Contents Recognition and Extraction for Heterogeneous Book Documents
(2013)

Existing work on book table of contents (TOC) recognition has been almost all on small size, application-dependent, and domain-specific datasets. However, TOC of books from different domains differ significantly in their visual layout and style, making TOC recognition a challenging problem for a large scale collection of heterogeneous books. We observed that TOCs can be placed into three basic styles, namely "flat", "ordered", and "divided", giving insights into how to achieve effective TOC parsing. As such, we propose a new TOC recognition approach which adaptively decides the most appropriate TOC parsing rules based on the classification of these three TOC styles. Evaluation on large number, over 25,000, of book documents from various domains demonstrates its effectiveness and efficiency.
Modeling online reviews with multi-grain topic models
(2008)

In this paper we present a novel framework for extracting the ratable aspects of objects from online user reviews. Extracting such aspects is an important challenge in automatically mining product opinions from the web and in generating opinion-based summaries of user reviews [18, 19, 7, 12, 27, 36, 21]. Our models are based on extensions to standard topic modeling methods such as LDA and PLSA to induce multi-grain topics. We argue that multi-grain models are more appropriate for our task since standard models tend to produce topics that correspond to global properties of objects (e.g., the brand of a product type) rather than the aspects of an object that tend to be rated by a user. The models we present not only extract ratable aspects, but also cluster them into coherent topics, e.g., 'waitress' and 'bartender' are part of the same topic 'staff' for restaurants. This differentiates it from much of the previous work which extracts aspects through term frequency analysis with minimal clustering. We evaluate the multi-grain models both qualitatively and quantitatively to show that they improve significantly upon standard topic models.
Comparing Twitter and Traditional Media Using Topic Models
(2011)

Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types. Our comparisons show interesting and useful findings for downstream IR or DM applications.
Earthquake shakes Twitter users: real-time event detection by social sensors
(2010)

Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.
TwitterReporter: Breaking News Detection and Visualization through the Geo-Tagged Twitter Network
(2011)

The Twitter social network provides a constant stream of concise data, useful within both geospatial and temporal domains. Previous studies have attempted to mine and find recent news topics within the data set. Although the efforts were a step in the right direction, we believe more is needed to accomplish a useful and interesting task: using live Twitter data to automatically identify breaking news events in near real-time. In this paper, we present methods to collect data, identify breaking news topics, and display results in a geo-temporal visualization. Our goal is to provide a solution that discovers breaking news faster than traditional reporting mediums.
A Resource-Driven Approach for Anchoring Linguistic Resources to Conceptual Spaces
(2016)

In this paper we introduce the TTCS system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. TTCS takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces (a geometric framework for common-sense knowledge representation and reasoning). The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.
Quickly Boosting Decision Trees - Pruning Underachieving Features Early
(2013)

Boosted decision trees are among the most popular learning techniques in use today. While exhibiting fast speeds at test time, relatively slow training renders them impractical for applications with real-time learning requirements. We propose a principled approach to overcome this drawback. We prove a bound on the error of a decision stump given its preliminary error on a subset of the training data; the bound may be used to prune unpromising features early in the training process. We propose a fast training algorithm that exploits this bound, yielding speedups of an order of magnitude at no cost in the final performance of the classifier. Our method is not a new variant of Boosting; rather, it is used in conjunction with existing Boosting algorithms and other sampling methods to achieve even greater speedups.
Holographic Photolysis for Multiple Cell Stimulation in Mouse Hippocampal Slices
(2010)

BACKGROUND
Advanced light microscopy offers sensitive and non-invasive means to image neural activity and to control signaling with photolysable molecules and, recently, light-gated channels. These approaches require precise and yet flexible light excitation patterns. For synchronous stimulation of subsets of cells, they also require large excitation areas with millisecond and micrometric resolution. We have recently developed a new method for such optical control using a phase holographic modulation of optical wave-fronts, which minimizes power loss, enables rapid switching between excitation patterns, and allows a true 3D sculpting of the excitation volumes. In previous studies we have used holographic photololysis to control glutamate uncaging on single neuronal cells. Here, we extend the use of holographic photolysis for the excitation of multiple neurons and of glial cells.


METHODS/PRINCIPAL FINDINGS
The system combines a liquid crystal device for holographic patterned photostimulation, high-resolution optical imaging, the HiLo microscopy, to define the stimulated regions and a conventional Ca(2+) imaging system to detect neural activity. By means of electrophysiological recordings and calcium imaging in acute hippocampal slices, we show that the use of excitation patterns precisely tailored to the shape of multiple neuronal somata represents a very efficient way for the simultaneous excitation of a group of neurons. In addition, we demonstrate that fast shaped illumination patterns also induce reliable responses in single glial cells.


CONCLUSIONS/SIGNIFICANCE
We show that the main advantage of holographic illumination is that it allows for an efficient excitation of multiple cells with a spatiotemporal resolution unachievable with other existing approaches. Although this paper focuses on the photoactivation of caged molecules, our approach will surely prove very efficient for other probes, such as light-gated channels, genetically encoded photoactivatable proteins, photoactivatable fluorescent proteins, and voltage-sensitive dyes.
Efficient RANSAC for Point-Cloud Shape Detection
(2007)

In this work we present an automatic algorithm to detect basic shapes in unorganized point clouds. The algorithm decomposes the point cloud into a concise, hybrid structure of inherent shapes and a set of remaining points. Each detected shape serves as a proxy for a set of corresponding points. Our method is based on random sampling and detects planes, spheres, cylinders, cones and tori. For models with surfaces composed of these basic shapes only, e.g. CAD models, we automatically obtain a representation solely consisting of shape proxies. We demonstrate that the algorithm is robust even in the presence of many outliers and a high degree of noise. The proposed method scales well with respect to the size of the input point cloud and the number and size of the shapes within the data. Even point sets with several millions of samples are robustly decomposed within less than a minute. Moreover the algorithm is conceptually simple and easy to implement. Application areas include measurement of physical parameters, scan registration, surface compression, hybrid rendering, shape classification, meshing, simplification, approximation and reverse engineering.
Multi-Camera Multi-Person Tracking for EasyLiving
(2000)

While intelligent environments are often cited as a reason for doing work on visual person-tracking, really making an intelligent environment exposes many realworld problems in visual tracking that must be solved to make the technology practical. In the context of our EasyLiving project in intelligent environments, we created a practical person-tracking system that solves most of the real-world problems. It uses two sets of color stereo cameras for tracking multiple people during live demonstrations in a living room. The stereo images are used for locating people, and the color images are used for maintaining their identities. The system runs quickly enough to make the room feel responsive, and it tracks multiple people standing, walking, sitting, occluding, and entering and leaving the
Early neurologic assessment in preterm-infants: integration of traditional neurologic examination and observation of general movements.
(2008)

OBJECTIVE
To evaluate the possible additional benefit in terms of prognostic accuracy of an integrated application of a traditional scorable method of neurologic examination and the Prechtl's method of qualitative assessment of general movements (GMs) in a large population of 903 consecutive preterm infants.


STUDY DESIGN
Infants were enrolled from the Intensive Care Unit of the University of Catania. Inclusion criteria were a gestational age below 37 weeks and the absence of genetic disorders. All infants underwent serial ultrasound and at 3 months performed both the GMs assessment and the Hammersmith Infant Neurologic Examination (HINE). Outcome was assessed at 2 years by the Touwen neurologic examination and the Clinical Adaptive Test/Clinical, Linguistic and Auditory Milestone Scale.


RESULTS
The integration of the two methods was shown to be more effective than the single assessments in predicting neurologic outcome. The additional benefit of combining the two approaches was particularly clear for the discrimination between unilateral and bilateral cerebral palsy.


CONCLUSIONS
The integrated use of a scorable neurological examination and Prechtl's assessment of GMs can improve early prediction of neurodevelopmental outcome in preterm infants and should complement other clinical and instrumental exams in follow-up programs.
Physics-based hand interaction with virtual objects
(2015)

We propose an intuitive user interaction system with virtual objects by directly using user's hands through a physics simulation. This system aims to provide a more direct way to manipulate and interact with virtual objects and to induce more realistic reactions of the virtual objects in the interaction than the existing methods. The key contribution of this paper is to model the deformations of the user's hands in two ways: global deformation by the hand poses and the local deformation by contacts with objects. By the two deformation models, objects can be manipulated with better stability and robustness without any predefinition of interaction methods depending on the object shapes, by solely using a physics simulation. The experiment shows that the proposed method enables a user to intuitively manipulate virtual object accurately.
Multiscale Shear-Lag Analysis of Stiffness Enhancement in Polymer-Graphene Nanocomposites.
(2017)

Graphene and other two-dimensional (2D) materials are of emerging interest as functional fillers in polymer-matrix composites. In this study, we present a multiscale atomistic-to-continuum approach for modeling interfacial stress transfer in graphene-high-density polyethylene (HDPE) nanocomposites. Via detailed characterization of atomic-level stress profiles in submicron graphene fillers, we develop a modified shear-lag model for short fillers. A key feature of our approach lies in the correct accounting of stress concentration at the ends of fillers that exhibits a power-law dependence on filler ("flaw") size, determined explicitly from atomistic simulations, without any ad hoc modeling assumptions. In addition to two parameters that quantify the end stress concentration, only one additional shear-lag parameter is required to quantify the atomic-level stress profiles in graphene fillers. This three-parameter model is found to be reliable for fillers with dimensions as small as ∼10 nm. Our model predicts accurately the elastic response of aligned graphene-HDPE composites and provides appropriate upper bounds for the elastic moduli of nanocomposites with more realistic randomly distributed and oriented fillers. This study provides a systematic approach for developing hierarchical multiscale models of 2D material-based nanocomposites and is of particular relevance for short fillers, which are, currently, typical of solution-processed 2D materials.